PhD Student Bio

I am an ELLIS PhD student at the Helmholtz AI Institute in Munich, where I am supervised by Dr. Vincent Fortuin. As part of the ELLIS program, I am also supervised by Dr. Mark van der Wilk, at the University of Oxford. Next to a BSc in Econometrics from the University of Amsterdam, I hold an MSc in Statistical Sciences from the University of Oxford.

My PhD project aims to develop adaptive modeling techniques leveraging Meta-Learning algorithms to accelerate scientific research. In order to make Machine Learning methods useful for scientific discovery, they should be tailored to the problem setting of scientific research. This usually includes an underlying physical system, scarce and expensive data, availability of domain knowledge and the need for uncertainty quantification.

I am interested in Meta-Learning, as it enables learning at a higher level, extracting patterns from related tasks to improve performance on new, unseen tasks. This allows such models to learn key properties of an underlying physical system, making Meta-Learning attractive in scientific settings.

By incorporating techniques from Bayesian Deep Learning, we can infuse uncertainty estimation and online learning capabilities into Meta-Learning frameworks. Uncertainty quantification is vital where mistakes are costly and can aid scientists in their decision-making process. This interaction of scientists with Machine-Learning models is highly desirable and should be facilitated by model design.